Method and system for diagnosing a condition of an engine using lubricating fluid analysis

ABSTRACT

Methods and systems for diagnosing a condition of an engine based on lubrication fluid analysis are disclosed. One embodiment of the methods comprises: receiving input data representative of a respective geometric parameter and a respective chemical composition for a plurality of particles filtered from a sample of fluid obtained from the engine; generating data representative of a mass of material of a chemical composition category in one or more of the particles; comparing the mass of material of the chemical composition category with reference data; and generating output data representative of a diagnosis of the condition of the engine.

CROSS REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. patent application Ser. No.14/962,389 filed on Dec. 8, 2015, the contents of which are herebyincorporated by reference.

TECHNICAL FIELD

The disclosure relates generally to engine diagnostics and moreparticularly to methods and systems for diagnosing engine conditionsusing oil (or other lubricant) analysis.

BACKGROUND OF THE ART

The analysis of engine oil or other lubricant for the purpose ofidentifying premature component wearing has been performed for severaldecades using optical atomic spectroscopy (e.g., atomic emissionspectroscopy (AES), as well as its predecessor that has been in use as afield monitoring method, atomic absorption spectroscopy (AAS)). Thistechnology has been the basis for the military aviation's SpectroscopicOil Analysis Program (SOAP). However, this technology can sometimes lackof repeatability among different equipment and does not considerparticles greater than 5 μm in diameter. Furthermore, SOAP typicallyuses optical atomic spectroscopy, which is an elemental analysis of thetotal oil sample and typically does not characterize individualparticles in the sample.

Rotrode Filter Spectroscopy has been introduced in 1980 and it wascommercialized by Spectro Incorporated (Littleton, Mass.). The methodfocuses mainly on the analysis of large particles in the oil and hasproven to be effective to establish the source of wear material in asystem already generating wear material [1].

Scanning electron microscope (SEM) equipped to perform X-rayspectroscopy has been used to characterize individual particles [2] forwear mode indicators. However, SEM has been considered to be unsuitablefor routine monitoring of machine condition, for example as reported inWhitlock [3].

SUMMARY

In one aspect, the disclosure describes a computer-implemented methodfor diagnosing a condition of an engine, the method comprising:

receiving input data representative of a respective geometric parameterand a respective chemical composition for a plurality of particlesfiltered from a fluid sample obtained from the engine;

using one or more data processors:

-   -   from the input data, generating data representative of a mass of        material of a chemical composition category in one or more of        the particles;    -   comparing the mass of material of the chemical composition        category in the one or more particles with reference data; and    -   based on the comparison, generating output data representative        of a diagnosis of the condition of the engine.

In another aspect, the disclosure describes a method for diagnosing acondition of a gas turbine engine, the method comprising:

using a plurality of particles filtered from a sample of usedlubricating fluid obtained from the gas turbine engine, generating inputdata representative of a respective geometric parameter and a respectivechemical composition for each of the plurality of particles;

using the input data, generating data representative of a cumulativemass of material of a chemical composition category in the plurality ofparticles;

comparing the cumulative mass of material of the chemical compositioncategory with reference data; and

based on the comparison, generating output data representative of adiagnosis of the condition of the engine.

In some example aspects, the present disclosure provides a system forfluid analysis, the system may include a processor and a memorycontaining machine-readable instructions for execution by the processor,the machine-readable instructions causing the processor to carry out themethods described herein.

In some example aspects, the present disclosure provides anon-transitory computer-readable medium or media embodyingcomputer-executable instructions configured for causing one or moreprocessors to carry out the methods described herein.

Further details of these and other aspects of the subject matter of thisapplication will be apparent from the detailed description and drawingsincluded below.

DESCRIPTION OF THE DRAWINGS

Reference is now made to the accompanying drawings, in which:

FIG. 1 is a schematic diagram of an exemplary system for diagnosing acondition of an engine such as a gas turbine engine;

FIG. 2 is a flowchart illustrating an exemplary method for failureprediction using fluid analysis;

FIG. 3 illustrates an example of different alloy zones that may be usedfor categorizing particles;

FIG. 4 is a flowchart illustrating an exemplary method for diagnosing acondition of an engine;

FIG. 5A shows a schematic representation of a top plan view of anexemplary approximated shape of a particle filtered from a fluid sample;

FIG. 5B shows a schematic representation of a perspective view of anexemplary approximated shape of a particle filtered from a fluid sample;

FIG. 6 is a flowchart illustrating a method for diagnosing a conditionof an engine based on a total mass of one or more elements in particlesin a sample of lubricating fluid; and

FIG. 7 is a flowchart illustrating a method for diagnosing a conditionof an engine based on a total mass of one or more alloys or materialtypes in particles in a sample of lubricating fluid.

DETAILED DESCRIPTION

The present disclosure relates to engine diagnostics using lubricatingfluid analysis. In various aspects disclosed herein, such lubricatingfluid analysis may use chemical composition of particles as well asgeometric parameters (e.g., size and shape) and/or mass of particles foruse in diagnosing a condition of an engine.

Recently developed engines, such as gas turbine engines for aircraftapplications, may produce debris (e.g., metallic debris) in oil samplesat a levels of concentration and at particle sizes below the operatingzone of conventional oil analysis techniques. However, the analysis ofsuch debris may still be useful in diagnosing conditions of such enginesand failure prediction. For example, SOAP typically requires debris tobe present on the order of parts per million (ppm) however, debris atthe parts per billion (ppb) level may also be of interest for enginediagnostics.

Oil analysis has been performed for predictive maintenance (e.g., onengines) for more than fifty years but has limited capability indetecting abnormal behavior early in the process. For example, usingconventional techniques, failure is typically detected only hours beforea problem occurs, resulting in the need for the engine operator tosubmit an oil sample at short time intervals (e.g., every 10 to 50hours) to have a chance to capture the indication of failure before theactual failure occurs. Such a high frequency of sampling may not bepractical or economical for an aircraft operator.

Considering the direct and indirect costs of any engine failure andunplanned engine removal, there is a need for methods and systems thatmay be able to predict failure sufficiently in advance, in order for theappropriate tasks (e.g., corrective action), such as maintenance and/ordecommissioning, to be scheduled and carried out.

One conventional approach for monitoring engine material wear is toperform an analysis on particles extracted from the engine filter; wherethe extracted particles are then analyzed by SEM. This method isrelatively costly because the engine filter is typically not reused.Further, such method may not be practical considering that removing theengine filter may be time consuming. For such reasons, among others,filter analysis typically is not performed frequently and is mainly usedto monitor engines already identified as potentially behavingabnormally. Filter analysis typically is not suitable for routinemonitoring of engines.

Another method is to analyze particles collected from the oil by amagnetic drain plug where the magnetic particles collected by the drainplug are removed and analyzed.

The disclosure describes methods and systems for engine diagnostics(e.g., failure prediction) using oil or other lubricating fluids. Invarious aspects, for example, the disclosure describes methods andsystems for failure prediction using analysis of particles obtained(e.g., filtered) from oil samples, such as from gas turbine engines.

In various aspects and examples, the disclosed methods and systems mayallow for analysis of engine oil (or other lubricant) samples in orderto detect abnormal behavior (e.g., wear mechanisms) without having toremove the engine filter. The disclosed methods and systems may bereferred to as “Pratt & Whitney Canada/Complete Oil Analysis Technology”(PWCOAT).

The filtering of the particles from an oil sample obtained from theengine may provide an indication of the wear mechanism(s) active at thetime the sample is collected instead of an indication of the wearmechanisms that were active at some time in the past since the fluidfilter of the engine was last replaced or since particles from themagnetic drain plug were last collected. Oil samples may be collected atintervals to monitor the status of a wear mechanism of interest in orderto determine whether the wear mechanism is progressing or subsiding inorder to determine whether some corrective action is required. Incomparison with the oil filter method, the use of an oil sample maypermit smaller particles to be considered in the diagnosis as opposed tobeing limited to a range of particle sizes captured by the filter. Incomparison with the magnetic drain plug method, the use of a fluidsample may permit for both magnetic and/or non-magnetic (e.g., aluminum)particles to be considered in the diagnosis as opposed to being limitedto only magnetic particles that would be captured by the magnetic plugof the engine.

Particles filtered from a fluid sample can provide information regardingwear mechanisms that are currently active at the time the sample isobtained instead of those that were active at some point in the past andwhich may or may not still be active. In other words, the particles canprovide a snapshot of the wear mechanism(s) active at the time when theparticles are still suspended in the fluid instead of having beencollected by a fluid filter of the engine or by a magnetic drain plug ofthe engine.

Accordingly, the disclosed methods and systems may be based on theanalysis of relatively small particles in oil that typically are notcaptured by a conventional 30 μm porosity fluid filter of the engine. Byextending the oil analysis to include smaller particles, the disclosedmethods and systems may provide better understanding of engine behaviorusing a relatively small oil sample. For example, it has been estimatedthat there are, on average, about one thousand particles sized between0.5 μm to 30 μm per ml of a typical sample, which can be analyzed topredict engine behavior and which typically are not considered inconventional filter analysis.

In some embodiments, the disclosed methods and systems may thus providediagnostic and analytical tools based on analysis of particles influids, such as engine oil or other lubricants and may provide advancedetection of premature wear on specific engine parts and/or detection offailure mechanisms. In some embodiments, the disclosed methods andsystems may be suitable for failure prediction for gas turbine enginesoperating in the field. The disclosed methods and systems may be usedfor prediction of other wear events including prediction of events otherthan failure using analysis of any suitable lubricating fluid of theengine. For example, the disclosed methods and systems may be used toidentify any contaminants that have been introduced into a lubricatingsystem (e.g., by usage in abnormal conditions or by a problem duringmaintenance). The disclosed methods and systems may also be used todetect any abnormal behavior of an engine component in contact with alubrication fluid system, for example.

In various embodiments, the methods and systems disclosed herein maymake use of methods, steps and components of systems described in USPatent Publication No. 2014/0121994 A1, which is incorporated herein byreference in its entirety.

FIG. 1 is a schematic diagram of an exemplary system 100 for diagnosinga condition of an engine such as a gas turbine engine and which uses afluid for lubricating some of its components such as bearings. System100 may comprise one or more computer(s) (referred hereinafter as“computer 112”) and suitable data acquisition equipment 114 of known orother type. Computer 112 may comprise one or more data processors 116which may be any programmable data processing apparatus of known orother type, or other devices to cause a series of operational steps tobe performed by computer 112 or other device(s) to produce one or morecomputer-implemented methods.

Computer 112 may also comprise one or more memories (referredhereinafter as “memory 118”), which may include a suitable combinationof any type of computer memory that is located either internally orexternally such as, for example, random-access memory (RAM), read-onlymemory (ROM), compact disc read-only memory (CDROM), electro-opticalmemory, magneto-optical memory, erasable programmable read-only memory(EPROM), and electrically-erasable programmable read-only memory(EEPROM), Ferroelectric RAM (FRAM) or the like. Memory 118 may compriseany storage means (e.g. devices) suitable for retrievably storingmachine-readable instructions 120 executable by data processor 116.Memory 118 may comprise tangible, non-transitory medium.

Computer 112 and data acquisition equipment 114 may be considered partof an (e.g., SEM) workstation 122. Accordingly, data acquisitionequipment 114 may comprise an SEM and other related devices, althoughany other suitable devices/methods for extracting the relevantinformation from particles 124 filtered from lubricating fluid sample126 may be used. In some embodiments, data acquisition equipment 114 maycomprise an SEM and an X-Ray Fluorescence (XRF) detector for carryingout particle analysis. For example, data acquisition equipment 114 maycomprise an automated SEM, such as that from Aspex Corporation. In someembodiments, the automated SEM may not require the presence of a humanto select the particle(s) 124 that will be analyzed. In someembodiments, software and/or hardware included in workstation 122 mayautomatically recognize the presence of a particle 124 and may thenautomatically move a stage and/or an electron beam to the particle(s)124 on which to perform the analysis.

System 100 may be used to conduct analysis of particles 124 filteredfrom lubricating fluid sample 126. Data acquisition equipment 114 may beused to analyze filtered particles 124 and generate input data 128.Input data 128 may be processed using computer 112 according toinstructions 120 in order to generate output data 130. In someembodiments, output data 130 may be representative of a diagnosis of thecondition of the engine and may be delivered to a user of system 100 orother authorized party via output device(s) 132 (e.g., one or morescreens and/or printers) for displaying and/or otherwise providing areport of the result(s) of the diagnosis. System 100 may include one ormore input devices (e.g., keyboard and mouse) for receiving user input,as well as one or more data ports and/or communication ports forreceiving and/or transmitting data (e.g., wirelessly or through wiredconnections) from/to other processors, systems and/or devices.Processing of input data 128 by computer 112 may make use of referencedata 134 for comparison purpose. It is understood that processing ofinput data 128 may be performed using one or more processors external toworkstation 122.

FIG. 2 is a flowchart illustrating an exemplary method 200 fordiagnosing a condition (e.g., predicting a failure) of an engine usinganalysis of fluid sample 126, such as engine oil or other lubricant ofthe engine. Method 200 may be carried out entirely or in part usingsystem 100 based on instructions 120. In some embodiments, depending onthe configuration of system 100, some or all of method 200 may beautomated (e.g., computer-implemented).

At 205, fluid sample 126 (e.g., an oil or other lubricant sample from anaircraft engine) is obtained. In the example of an oil sample from anaircraft engine, fluid sample 126 may be collected by maintenancepersonnel of the associated aircraft operator. Accordingly, fluid sample126 may comprise a relatively small volume of fluid extracted from arelatively larger source of fluid of the engine. In some embodiments,more than one sample 126 may be collected from the engine. In someembodiments, a relatively small amount of oil (e.g., 25 ml or less) maybe sufficient. The quantity of oil obtained may be selected in order toobtain a desired number of particles 124 for analysis. For example, itmay be known or expected that a particular type of engine should have acertain density of particles in the oil after a certain number ofoperating hours. Accordingly, the volume of the fluid sample 126obtained may thus be determined in order to obtain at least 1000particles 124, for example. The frequency of sampling may be determinedbased on the flight/operating hours per year, the maturity of theengine, the typical behavior of the engine type and/or the history ofunscheduled engine removal for that engine type, for example. The sample126 may be obtained and prepared using any suitable known or othermethod.

At 210, sample 126 is filtered using any suitable known or other methodto obtain particles 124 from the fluid sample 126. For example, acollected fluid sample 126 may be filtered using a very fine filter,such as a 0.22 μm filter, in order to filter out even very smallparticles 124 (e.g., particles sized as small as 0.5 μm in diameter orsmaller). Using such a filter, a fluid sample 126 of about 25 ml may besufficient to produce a surface sample of particles 124 of about 16 mmin diameter and suitable for data acquisition via workstation 122 forexample. The particles 124 obtained may range in size from about 0.5 μmto about 1600 μm, for example, although smaller or larger particles 124may also be obtained and used in method 200.

The volume of fluid sample 126 used and the size of the sample ofparticles 124 prepared may vary according to the number of particles 124in the fluid sample 126. The volume of fluid sample 126 that is used maybe determined based on the type of engine and/or the expected normallevels of particles in the oil. In some embodiments, the density ofparticles 124 of the surface sample of particles may be about 500particles 124 per mm², which may be the maximum density that can beused, to reduce or avoid the likelihood of overlapping particles 124. Itmay be useful to reduce or avoid overlapping particles since two or moreparticles that overlap with each other may be incorrectly identified asone large particle, which may lead to incorrect identification andelemental analysis. For example, depending on the density of the sampleof particles 124, about 5-10% of particles 124 analyzed may not beidentifiable typically due to overlapping and such particles 124 mayconsequently be excluded from the analysis. In some embodiments, thisexclusion rate may be acceptable.

At 215, some or all particles 124 filtered from fluid sample 126 areanalyzed using data acquisition equipment 114 to acquire input data 128from particles 124. Input data 128 may comprise one or more geometricparameters (e.g., shape, size, volume, one or more dimensions) and/orchemical composition information (e.g., element identification, alloyidentification, material type) for each particle 124 analyzed. The oneor more geometric parameters and the chemical composition may beacquired substantially automatically or semi-automatically depending onthe configuration of workstation 122. For example, in some embodiments,data acquisition equipment 114 may be at least partially controlled bycomputer 112. Any other suitable equipment may be used to generate inputdata 128.

In some embodiments, a subset of the particles 124 (e.g., 10% or less)may be analyzed and may be sufficient to provide a good representationof the sample of particles 124 as a whole. For example, input data 128acquired for the subset may be normalized to reflect/estimate theresults for the whole sample of particles 124. The analysis of a subsetof particles 124 may reduce processing time.

For an average fluid sample 126, about 1500 to 2000 particles 124 may beanalyzed. Suitable image analyzer software, such as those conventionallyused with SEM, may be used to collect data about particlecharacteristics and/or composition. Analysis of each particle 124 mayproduce a respective set of data for each particle 124. For example, insome embodiments, there may be up to 70 data points associated with eachparticle 124 for representing the various features of the particle 124(e.g., size, shape and composition among others). The total number ofdata points obtained from analysis of a single sample may besignificantly greater than in conventional oil analysis techniques.

Input data 128 obtained from this analysis may be further processed inorder to account for any measurement error and/or the possible presenceof contamination. This further processing may include categorizing theparticles 124 as described below.

At 220, each particle 124 may be categorized based on the determinedfeatures (e.g., geometric parameter and/or chemical composition). Theparticles 124 may be categorized in different categories, which may bedefined according to one or more of: chemical composition categories(e.g., elemental and/or alloy composition), geometric parameters (e.g.,size, morphology) and mass. For example, morphology of a particle 124may be determined by calculating an aspect ratio for the particle 124(e.g., length to width ratio, for example, where a ratio close to 1 mayindicate the particle 124 is close to having a spherical shape while alarger value, such as 10, may indicate that the particle 124 is close toa long fiber shape). For example, particles may be classified incategories such as “Environmental”, “Metallic”, “Non-metallic”,“Plating”, or “Miscellaneous”, among others. Each particle 124 may befurther categorized into sub-category levels. As an example, the“Metallic” category may have a level 1 sub-category of “Copper”, withinwhich may be level 2 sub-categories of “Bronze” and “Brass”. In someembodiments, five levels of decisions may be used to categorize eachparticle 124 into a specific level (e.g., metallic, copper, bronze,leaded bronze or machining chip). Categorization of particles 124 may bebased on, for example, the absolute chemical composition, the ratio ofsome elements, the correlation between a specific standard and particle124, the size of particle 124, the shape of particle 124 and/or the massof particle 124 or of some element(s) contained in particle 124.Categories may be defined according to different alloy compositions,association with one specific manufacturing process and/or associationwith one particular source (e.g., engine component), for example.Categories may also be defined by the elemental composition or singlematerial of the particles 124.

FIG. 3 illustrates an example of how categories may be defined withrespect to iron (Fe), chromium (Cr) and nickel (Ni) composition in analloy. The diagram is divided into different zones, corresponding todifferent categories. Dots on the diagram illustrate how exampleparticles 124 fall within different zones. The zones may be associatedwith a particular alloy and/or expected sources of the alloy. Forexample, zone 15 may be associated with M50, a bearing material. Aparticle that is categorized as belonging to the category of zone 15 maybe expected to originate from a bearing, and its presence may bepredictive of bearing wear and/or a failure mechanism related to bearingwear.

Categorization of each particle 124 may be carried out using analgorithm to match each particle to the appropriate category. Eachparticle may be compared against a historical standard for a category,in order to determine if that particle 124 belongs in that category.Example algorithms for carrying out this categorization include the useof a Cross Probability Match (CPM) Index, as well as logical exclusiontests.

The CPM may be understood as an evaluation of the product of thelikelihood of the unknown particle to be the standard (PvS) and thelikelihood of the standard to be the unknown particle (SvP). In thisexample, the likelihood of a match may be based on an evaluation of theelemental composition of the particle compared to that of a historicalstandard for that category or sub-category.

An example version of CPM uses a linear model, which is based on alinear contribution of each element equal to its concentration. The CPMlinear model may be described by the equation:

CPM_(ij) = P_(j)vS_(i) × S_(i)vP_(j) × 100 where${P_{j}{vS}_{i}} = {\sum\limits_{k}^{n}\left( {{\min\left\lbrack {\frac{E_{p_{k}}}{E_{s_{k}}},\frac{E_{s_{k}}}{E_{p_{k}}}} \right\rbrack}^{2} \times N_{S_{k}}} \right)}$${S_{i}{vP}_{j}} = {\sum\limits_{k}^{n}\left( {{\min\left\lbrack {\frac{E_{p_{k}}}{E_{s_{k}}},\frac{E_{s_{k}}}{E_{p_{k}}}} \right\rbrack}^{2} \times N_{P_{k}}} \right)}$$N_{P_{k}} = \frac{E_{P_{k}}}{\underset{l}{\sum\limits^{n}}\left( E_{P_{l}} \right)}$and$N_{S_{k}} = \frac{E_{S_{k}}}{\underset{l}{\sum\limits^{n}}\left( E_{S_{l}} \right)}$

and where i denotes the particle being categorized; j denotes thehistorical standard; n denotes the number of elements of interest usedto categorize the particle; Epk denotes the concentration of the elementk in the particle being categorized; Esk denotes the concentration ofthe element k in the historical standard being compared to; Npk denotesthe normalized concentration of the element k in the particle beingcategorized; Nsk denotes the normalized concentration of the element kin the historical standard; Tp denotes the summation of theconcentration of all elements of interest in the particle; Ts denotesthe summation of the concentration of all elements of interest in thehistorical standard; PvS denotes the probability for the particle to bethe historical standard; and SvP denotes the probability for thehistorical standard to be the particle.

Another example model is CPMQ, the square root version of CPM, which isbased on a contribution equal to the squared of its concentration. CPMQmay be used where an element is present in a high percentage (e.g.,greater than 5%) in an alloy belonging to the category of interest. TheCPM square root model may be described by the equation:

CPMQ_(ij) = P_(j)vS_(i)^(*) × S_(i)vP_(j)^(*) × 100 where${P_{j}{vS}_{i}^{*}} = {\sum\limits_{k}^{n}\left( {{\min\left\lbrack {\frac{E_{p_{k}}}{E_{s_{k}}},\frac{E_{s_{k}}}{E_{p_{k}}}} \right\rbrack}^{2} \times N_{S_{k}}^{*}} \right)}$${S_{i}{vP}_{j}^{*}} = {\sum\limits_{k}^{n}\left( {{\min\left\lbrack {\frac{E_{p_{k}}}{E_{s_{k}}},\frac{E_{s_{k}}}{E_{p_{k}}}} \right\rbrack}^{2} \times N_{P_{k}}^{*}} \right)}$$N_{P_{k}}^{*} = \frac{\sqrt{E_{P_{k}}}}{\underset{l}{\sum\limits^{n}}\left( \sqrt{E_{P_{l}}} \right)}$$N_{S_{k}}^{*} = \frac{\sqrt{E_{S_{k}}}}{\underset{l}{\sum\limits^{n}}\left( \sqrt{E_{S_{l}}} \right)}$

Using CPM or other suitable statistic techniques to categorize eachparticle 124 may, for example, allow for automation of particlecategorization. The use of CPM or other suitable statistical techniquesmay also allow for categorization of particles 124 while accounting forpossible measurement noise and/or contamination, for example.

In some embodiments, a category (also referred to as a group ofinterest) may further break down into one or more bins defined accordingto particle size ranges. For example, particles 124 may be categorizedin columns according to size (in μm, in the example shown) and in rowsaccording to composition. In some embodiments, particles 124 may also besorted into bins according to particle morphology. In some embodiments,there may be 84 categories and sub-categories. The categories may bedefined based on elemental composition, alloy type, particle origin, orany other suitable category of particle characteristics and composition.Categorizing particles 124 by size, shape and/or mass, as well ascomposition may allow for distinguishing between one failure mechanismthat is characterized by small particles 124 of a given alloy and adifferent failure mechanism that is characterized by large particles 124of the same given alloy, for example. Categorizing particles 124 intocategories other than simple elemental composition may also allow fordiscerning particle data patterns that may not be otherwise observed.

For example, a category may represent a generic type of alloy, and mayinclude one or more levels of sub-categories that may further split thecategory into finer categorization, for example as precisely as thealloy unified number (UNS) of the particles analyzed. For example, aspecific alloy may cover two or more categories and/or sub-categories.

Example categories and sub-categories include but are not limited to:

Environmental—sub-categories: calcium, sodium, CalSil (which mayoriginate from cement from an airstrip), dust—earth, talc, vermiculite(which may originate from packaging of the sample) and chlorides (withfurther sub-category NaCl).

Metallic—sub-categories: iron (which may include further sub-categoriesof different composition zones such as different steels, and other alloytypes), nickel, titanium, copper (with further sub-categories such asbrass, bronze and leaded copper), zinc (which may originate fromgalvanized coating found in the engine filters and is typically foundwith iron and phosphorus particles also), aluminum, magnesium, cobaltand chromium.

Non-metallic—sub-categories: aluminum/silicon, silicon/aluminum,silicon/magnesium, magnesium/aluminum, fiberglass, asbestos, filterfibers, glass beads and silica.

Others—sub-categories: MoS2, grease with MoS2, lead and contaminatedsilver.

Plating—sub-categories: tin, silver, cadmium, copper,phosphate-manganese (AMS 2481) and chromium.

Not categorized.

These exemplary categories may be predefined based on knowledge orexpectation of what kind of particles 124 would be obtained from anfluid sample 126 of a given engine type. The categories may also bedefined based on the analysis of the samples. For example, if it appearsthat most of the particles 124 fall into a few categories,sub-categories may be defined for those few categories in order to morefinely categorize the particles 124. The defined categories may bedifferent for different engine types and/or at different total operatinghours, for example.

At 225 of FIG. 2, the data obtained from categorization of particles 124is compared with reference data 134, which may include historical dataassociated with the engine type (e.g., other engines, fleet data) and/orany data from previous analyses of the same engine. This comparison maybe based on a quantification in each category (e.g., a count ofparticles of certain characteristics, such as certain size, morphologyand/or mass, which may be based on the sorting of particles into binswithin each category, and may include normalizing the count to a 25 mLsample and for 100% of the area analyzed), to obtain a set ofcategorized data. The categorized data in each category and/orsub-category, as well as categorized data representative of allcategories, may be compared with the historical data.

In an example where an fluid sample 126 from an engine is beinganalyzed, data obtained for the specific fluid sample 126 may becompared with other historical data obtained from engines of the same orsimilar type obtained at equivalent or similar operating hours and/orequivalent or similar operating conditions (e.g., running in a dry orsandy environment vs. a wet environment).

Historical data may be collected as part of the disclosed methods andsystems, may be collected using other techniques, may be collected aspart of routine maintenance, may be derived from previous records andengine specifications, or may be obtained by any other suitable means.One or more sets of historical data may be represented by an aggregateor general historical model of expected engine wear and/or failuremechanisms for engines of a particular engine type. The historical modelmay be a simple average of all data for a given engine type at a givenoperating age, for example. In some embodiments, a historical model mayinclude an average of all data, expunged of six-sigma results. The modelmay be adjusted over time as more historical data sets are added to themodel. A model based on a larger population of historical data may be amore accurate and precise predictor of engine failure than a model basedon a smaller population of historical data. Historical data may includedata from different engines of the same engine type at the same orsimilar operating hours (also referred to as latitudinal data orcross-sectional data), and may also include data from the same engine atdifferent operating hours (also referred to as longitudinal data).

The comparison between the sample data and the historical data may becarried out using any suitable statistical methods. For example, anycategory with sample data values that fall outside a three sigma rangeof the historical data may be flagged or highlighted. For example, for agiven category the comparison may use the calculation: (sampledata−average data)/standard deviation.

In some embodiments, a composite variation value may be calculated basedon the difference between the sample and historical data, for exampleexpressed in standard deviation and/or a weight assigned to eachcategory. For example, a composite index for a low alloy may becalculated as:

${CI}_{LowAlloy} = {\sum\limits_{i = 10}^{12}\left\lbrack {\sum\limits_{j = 1}^{5}{w_{ij}S_{ij}}} \right\rbrack}$

Where: i is the zone category; j is the particle size category; Sij isthe deviation expressed in standard deviation for the particles of thecategory size j from the zone i; Wij is the weight attributed for theparticles of the category size j from the zone I, and the sum of allweight is equal to 1. The weighting factor may be based on historicaldata and/or the engine model.

At 230, a prediction of any possible future failure(s) and/or failuremechanism(s) is generated. This prediction may be based on the resultsof the comparison with historical data. For example, a category flaggedas being outside a three sigma range of the historical data may beconsidered to be predictive of failure of an engine part associated withthat category. This may be based on trend analysis of analysis resultsfrom the same engine or engine type. Corrective action (e.g., engineremoval or increased frequency of testing) may be determined based onthe engine history and/or expected performance of the engine type, forexample. In some embodiments, prediction of expected failure(s) and/orfailure mechanism(s) may involve review by an expert, a technicalspecialist and/or an operator. Examples of predicted mechanism offailure include excess vibration, bearing wear, external contaminationfollowing engine maintenance, bearing rubbing, gear degradation, andbearing cage and race degradation, among others.

A prediction of failure may be based on a combination of two or morefactors. For example, comparison results that indicate a given enginehas a greater than normal number of small particles in ferrous alloyzone 20 (corresponding to stainless steel) may indicate excessive enginevibration. This result, in addition to comparison data that indicatesthe engine has an increased number of particles in zone 11(corresponding to a bearing cage) and the presence of submicronparticles in zone 15, may together indicate that the engine isexhibiting problems with a bearing when compared to historical data.

A particular engine type may be known to have certain failure patterns,based on historical data. By comparing data for a given engine belongingto that category/sub-category with historical data for thatcategory/sub-category, a prediction may be generated to indicate whenthe given engine is expected to fail and/or the expected failuremechanism for the given engine. For example, historical data may revealcertain patterns of particle characteristics over time. By comparing thesample particle data of the given engine with the historical patternover time, a prediction may be generated of where the given engine is inthe expected timeline for engine failure.

In some embodiments, two or more engine types may share the same orsimilar mechanism of failure. In such cases, historical data of oneengine type may be used for failure prediction of the other engine type,historical data of two or more engine types may be compiled together,and/or the sample historical data may be used for failure prediction oftwo or more engine types, for example.

Using the generated prediction of failure, appropriate action may betaken. For example, where failure of a particular part has beenpredicted, that part may be replaced and/or monitored with greaterfrequency. Or, where failure of the engine has been predicted, thatengine may be placed on a tighter maintenance and/or oil analysisschedule. For example, the disclosed methods may include performing amaintenance or pre-maintenance action on the engine. Maintenance orpre-maintenance actions that may be performed include, for example,flagging the engine for maintenance (e.g., in a maintenance file),generating a notification to alert a user for the need to performmaintenance, scheduling maintenance for the engine, and performing theappropriate maintenance, among others. The maintenance orpre-maintenance action performed may be dependent on the generatedprediction.

The generated prediction may be recorded and saved for further actionand/or future reference. The results may also be added to the historicaldata. An electronic image of the sample may also be stored for futurereference and/or further processing.

FIG. 4 is a flowchart illustrating method 400 for diagnosing a conditionof an engine such as a gas turbine engine of an aircraft. In someembodiments, method 400 may be used to predict a failure of one or morecomponents of the engine. Method 400 may be performed in its entirety orin part using system 10 shown in FIG. 1 and described above. Method 400or part(s) thereof may be performed in combination with part(s) of othermethods disclosed herein. Aspects of the systems and methods describedabove may also apply to method 400. Some or all of method 400 may becomputer-implemented.

Method 400 may comprise the determination of a mass of one or moreparticles 124 and/or the mass of one or more elements, alloys ormaterial types contained in one or more particles 124. The massinformation in combination with a chemical composition category of aparticle or a group of particles 124 filtered from lubrication fluidsample 126 may be used to diagnose a condition of engine and determinewhether a (e.g., corrective) action should be initiated. For example,method 400 may provide an indication of how much mass of a particularelement (e.g., Fe, Cr, Al, Ni), of a particular material type (e.g.,ferrous or non-ferrous, tool steel), and/or of a particular alloy (e.g.,M50) is present in sample 126 of lubricating fluid. For example, method400 may provide an indication of how many grams (or micrograms) of aparticular chemical composition category is present per liter oflubricating fluid (e.g., Iron (Fe) 0.003 μg/L, Chromium (Cr) 0.0004μg/L, M50 0.002 μg/L, tool steel 0.005 μg/L). Performing method 400using samples 126 of lubricating fluid obtained (i.e., collected) froman engine at different times in order to monitor the mass of one or moreelements, material types or alloys in the lubricating fluid of theengine may provide an indication as to whether a particular condition(e.g., wear mechanism) is active and whether the condition isprogressing. Also, since particles 124 are filtered from fluid sample126, the information extracted from the filtered particles 124 isrepresentative of the wear mechanisms that were active at the time thesample was obtained since the particles 124 were still in suspension inthe lubricating fluid.

In various embodiments, method 400 may comprise: receiving input data128 representative of a respective geometric parameter and a respectivechemical composition for a plurality of particles 124 filtered fromsample 126 of used lubricating fluid obtained from the engine (see block402); from input data 128, generating data representative of a mass ofmaterial of a chemical composition category in one or more of theparticles 124 (see block 404); comparing the mass of material of thechemical composition category with reference data 134 (see block 406);and based on the comparison, generating output data 130 representativeof a diagnosis of the condition of the engine (see block 408).

As explained above, reference data 134 may comprise historical dataassociated with the particular engine from which lubricating fluidsample 126 was obtained and/or associated with one or more other engines(e.g., fleet data). The first chemical composition category may berepresentative of an element, a material type or an alloy.

The geometric parameter (or one or more geometric parameters) may beused as a basis for calculating the mass of each particle 124. It isunderstood that the calculation of the mass may be an estimate of themass of particle 124 made based on the geometric parameter associatedwith particle 124 and, optionally, based on one or more assumptions ofthe overall shape of particle 124. In some embodiments, the geometricparameter may include a surface area of particle 124, a size of particle124 and/or one or more dimensions of particle 124. Using such one ormore geometric parameters, an estimation of volume of the particle 124in question may be computed by computer 112. Alternatively, a volume ofthe particle 124 may be included in input data 128 in some embodiments.Using the volume of particle 124, method 400 may comprise generatingdata representative of a mass of material using the known density orspecific gravity associated with the chemical composition category towhich particle 124 has been assigned.

The mass information may also be computed taking into consideration thepercent concentration of a particular element, alloy or material type inparticle 124 so that the mass of material in particle 124 for aparticular chemical composition category may be prorated based on thechemical composition of the particle 124. It is understood that, in somesituations, a particle 124 may contain materials from more than onechemical composition category due to interactions between materials andalso depending on how the chemical categories are defined (e.g.,elemental, by alloy or by material type). For example, a particle 124containing mainly stainless steel may have a concentration of about 11%chromium content by mass. Accordingly, the concentration of chromium inparticle 124 may be taken into consideration when computing the mass ofchromium in that particle 124. For example, for a particle having anestimated mass of 0.0034 μg and an estimated composition of tungsten(VV) of 2.0% by mass, the estimated mass of tungsten (VV) in particle124 may be estimated to be 0.0034 μg×2.0%=0.000068 μg.

In some embodiments, the mass determined (estimated) may comprise acumulative (total) mass of all material of a particular chemicalcomposition category found in particles 124 of lubricating fluid sample126.

In some embodiments, the reference data 134 may comprises predefinedengine diagnosis rules obtained from a database stored in memory 118 orelsewhere and used for evaluation of the mass of material determined todiagnose the condition of the engine. Such rules may include ranges andthreshold masses per volume of lubricating fluid for different chemicalcomposition categories and which may be indicative of differentconditions (e.g., wear mechanisms, failure signs) of the engine.Accordingly, comparison of the mass and composition category informationwith reference data 134 may be useful in diagnosing a condition of theengine. In various embodiments such method of diagnosis using mass maybe conducted instead of conventional methods, or, may be conducted inconjunction with conventional methods in order to complement suchconventional methods.

FIG. 5A shows a schematic representation of a top plan view of anexemplary approximated ellipsoid shape of particle 124 filtered fromlubricating fluid sample 126. The ellipsoid shape (e.g., needle-shaped,rice grain-shaped) shown in FIG. 5A may have a length L, a width W,dimensions R1 and R2 and a height H (i.e., thickness) in the directionperpendicular to the page. FIG. 5A may also have a surface area Svisible from a vantage point located above particle 124 as would bedetected by data acquisition equipment 114 (e.g., SEM) for example.Width W may be smaller than length L. FIG. 5B shows a schematicrepresentation of a perspective view of an exemplary approximatedrectangular prism shape of particle 124 filtered from a lubricatingfluid sample 126. The rectangular prism shape shown in FIG. 5B may havea length L, a width W and a height H. FIG. 5B may also have a surfacearea S visible from a vantage point located above particle 124. Width Wmay be smaller than length L. Surface area S for both the ellipsoid andthe rectangular prism may be representative of an area of a footprint ofparticle 124 visible from a vantage point above particle 124 as opposedto the entire surface area surrounding particle 124. In someembodiments, surface area S may be provided in input data 128 togetherwith one or more of width W and length L.

In some embodiments, the shapes of FIGS. 5A and 5B may be used toestimate the volume of particle 124. For example, calculation of thevolume of particle 124 may be based on the assumption that particle 124has a shape approximating an ellipsoid shape resembling that shown inFIG. 5A and/or calculation of the volume of particle 124 may be based onthe assumption that particle 124 approximates a rectangular prism shaperesembling that shown in FIG. 5B. In some embodiments, the approximatedshape for particle 124 may be selected based on the on the geometricparameter (e.g., aspect ratio L/W) acquired in input data 128. For arectangular prism for example, the volume may be calculated as follows:Volume=L×W×H; or Volume=S×H. For an ellipsoid for example, the volumemay be calculated as follows: Volume+ 4/3×π×R1×R2×H/2.

The height H of particle 124 may be unknown from input data 128 but itmay be assumed in some situations that the height H is less than thewidth W because the likelihood of particle 124 resting on its narrowside (edge) during the acquisition of input data 128 may be small. Insome embodiments, it can be estimated that about 50% of particles 124will have a shape approximating a rectangular prism and about 50% ofparticles 124 will have a shape approximating an ellipsoid. In someembodiments, it can be estimated that about ⅓ of particles 124 will havea height H of about 0.5 W, about ⅓ of particles 124 will have a heightof about 0.25 W and the remaining ⅓ of particles 124 will have a heightof about 0.1 W. According to these assumptions and the data available ininput data 128, the average volume of a particle 124 may be estimated asfollows: Volume=(S×0.5 W+S×0.25 W+S×0.1 W+S×0.5 W× 4/6+S×0.25 W×4/6+S×0.1 W× 4/6)/6. For the sake of simplicity, the calculation of thevolume of particle 124 based on the assumptions made above may bereduced to: Volume=0.236 W, where W is smaller than length L. In someembodiments, the volume of particle 124 may be approximated as 0.25 W.It is understood that the above calculation of the volume of particle124 may be based on an average distribution of the particle shapedistribution. It is understood that the volume calculations could bedifferent for different particle shapes.

In various embodiments, method 400 may comprise the acquisition of inputdata 128 using data acquisition equipment 114 and/or other stepsdisclosed herein. For example, generating input data 128 may compriseperforming particle analysis using X-ray spectroscopy. For example,particles 124 may have a size between about 0.5 μm and about 1600 μm.

In some embodiments, method 400 may comprise: using a plurality ofparticles 124 filtered from sample 126 of used lubricating fluidobtained from the gas turbine engine, generating input data 128representative of a respective geometric parameter and a respectivechemical composition for each of the plurality of particles 124; usinginput data 128, generating data representative of a cumulative (i.e.,total) mass of material in particles 124 in a chemical compositioncategory; comparing the cumulative mass of material in the chemicalcomposition category with reference data 134; and based on thecomparison, generating output data 130 representative of a diagnosis ofthe condition of the engine.

In some embodiments, method 400 may comprise using output data 130 toinitiate a corrective action such as a maintenance procedure or somerecommended inspection task/schedule based on the output data. Asexplained above, reference data 134 may comprises predefined enginediagnosis rules obtained from a database. The initiation of suchaction(s) may, for example, comprise recommending to maintenance orother personnel that such action should be carried out.

FIG. 6 is a flowchart illustrating method 600 for diagnosing a conditionof an engine such as a gas turbine engine of an aircraft based on atotal mass of one or more elements in a sample 126 of lubricating fluid.Method 600 may be performed in its entirety or in part using system 10shown in FIG. 1 and described above. Method 600 or part(s) thereof maybe performed in combination with part(s) of other methods disclosedherein. Some or all of method 600 may be computer-implemented.

In various embodiments, method 600 may comprise: analyzing particles 124filtered from lubricating fluid sample 126 to generate input data 128(see block 602); determining the estimated mass of each particle 124analyzed (see block 604); calculating the mass of one or more elementsin each particle 124 (see block 606); calculating the total mass of theone or more elements in lubricating fluid sample 126 (see block 608);normalizing the total mass according to the initial volume oflubricating fluid in sample 126 (see block 610); establishing thecriticality of the engine condition by statistically comparing thenormalized total mass with results obtained from other samples (seeblock 612); comparing the normalized total mass with results from thesame source of lubricating fluid (i.e., another sample from the same oilof the same engine) to determine the evolution of the condition (seeblock 614); and recommending an action based on the criticality andevolution of the condition (see block 616).

FIG. 7 is a flowchart illustrating method 700 for diagnosing a conditionof an engine such as a gas turbine engine of an aircraft based on atotal mass of one or more alloys or material types in a sample 126 oflubricating fluid. Method 700 may be performed in its entirety or inpart using system 10 shown in FIG. 1 and described above. Method 700 orpart(s) thereof may be performed in combination with part(s) of othermethods disclosed herein. Some or all of method 700 may becomputer-implemented.

In various embodiments, method 700 may comprise: analyzing particles 124filtered from lubricating fluid sample 126 to generate input data 128(see block 702); determining the estimated mass of each particle 124analyzed (see block 704); calculating the mass of one or more specificalloys or material types in each particle 124 (see block 706);calculating the total mass of the one or more specific alloys ormaterial types in lubricating fluid sample 126 (see block 708);normalizing the total mass according to the initial volume oflubricating fluid in sample 126 (see block 710); establishing thecriticality of the engine condition by statistically comparing thenormalized total mass with results obtained from other samples (seeblock 712); comparing the normalized total mass with results from thesame source of lubricating fluid (i.e., another sample from the same oilof the same engine) to determine the evolution of the condition (seeblock 714); and recommending an action based on the criticality andevolution of the condition (see block 716).

The present disclosure also provides systems for carrying out thedisclosed methods. The disclosed methods and systems may allow formonitoring of an engine over time, and may allow for a timeline ofexpected failure mechanisms to be developed for that engine type.

The disclosed methods and systems may allow for more sensitive and/orearlier detection of possible engine failure, compared to conventionalmethods. For example, current oil analysis technologies may not identifythe composition of each particle, and a relatively high concentration(e.g., more than 10 ppm) of wear metal in the fluid sample may berequired to detect the presence of abnormal wearing. In someembodiments, the disclosed methods and systems may allow the detectionlimit of wear in bearing material for example to be reduced by a factorof 1000 or more, which may allow for earlier detection of a problem.

In the disclosed methods and systems, the size of the particles may alsobe taken into account, which may help to avoid potential error caused bythe presence of one big particle. For example, a spherical particle ofgear material that is 4 μm in diameter will give the same % Fe readingfor the total sample as 500 particles of bearing material each 0.5 μm indiameter. If the sizes of the individual particles are not taken intoaccount, this could lead to possible misdiagnosis of bearing wear asbeing gear wear.

Alternatively or in addition to particle size, the disclosed methods andsystems may consider the mass of material in one or more chemicalcomposition categories for the purpose of diagnosing a condition of anengine as described above. The use of mass may be useful in cases wherethe particles could contain materials from similar compositions and/orwhere there could be interactions between materials.

The conventional SOAP technique typically relies on elemental analysisusing emission/atomic absorption analysis of particles. The particlesanalyzed are typically limited to 2-3 μm or smaller. The result of SOAPis typically a quantification of elements (e.g., iron) by volume (e.g.,in ppm), without a consideration of particle size, mass, alloy type orshape of the particle, and may produce a relatively small number of datapoints (e.g., about 30 data points that describe the total quantities ofindividual elements in the total sample). In the present disclosure, inadditional to categories defined by elemental composition, categoriesmay be defined in other ways, such as by alloy type and expectedparticle origin (e.g., specific engine component that might be thesource of the particle), as well as by particle size, shape and/or mass.These other category definitions may help in identifying data patternsthat may not be discernible when categorizing particles only byelemental composition. Further, in some embodiments, the disclosedmethods and systems may consider characteristics of each individualparticle, rather than overall characteristics of the total sample.

Conventional oil analysis techniques typically are limited to analysisof relatively large particles (e.g., 30 μm or larger). The presentlydisclosed methods and systems may allow oil analysis to be carried outon large as well as smaller particles.

In various aspects and embodiments, the present disclosure may providethe ability to identify the composition and mass of wear material in alubricating fluid sample. In some embodiments, the disclosed methods andsystems may provide a low detection limit that may allow the detectionof abnormal wearing sufficiently early in the deterioration cycle,giving a chance to fix the problem during a planned maintenance. Earlyand/or rapid detection of a problem may also give the opportunity to fixthe root cause of the problem early, which may help to reduce the totalnumber of problematic engines in the field.

The above description is meant to be exemplary only, and one skilled inthe art will recognize that changes may be made to the embodimentsdescribed without departing from the scope of the invention disclosed.For example, although the disclosed method has been described in termsof a number of steps, certain steps may be omitted and/or rearranged inorder. Although the present disclosure makes reference to oil analysisfor predicting failure in an engine, analysis of other fluids (e.g.,other lubricants) for failure prediction in other components may becarried out using the disclosed methods and systems. The presentdisclosure is also intended to cover and embrace all suitable changes intechnology. Still other modifications which fall within the scope of thepresent invention will be apparent to those skilled in the art, in lightof a review of this disclosure, and such modifications are intended tofall within the appended claims. Also, the scope of the claims shouldnot be limited by the preferred embodiments set forth in the examples,but should be given the broadest interpretation consistent with thedescription as a whole.

REFERENCES

-   [1] M. Lukas, D. P. Anderson, Spectro Incorporated, Littleton, Mass.    “Rotrode Filter Spectroscopy, Does It have a Place in Commercial or    Military Oil Analysis Laboratory”.-   [2] K. J. Eisentraut, R. W. Newman, C. S. Saba, R. E. Kauffman,    and W. E. Rhine, “Spectrometrix oil analysis: detecting engine    failures before they occur”, Analytical Chemistry 56, August 84,    1086A-1094A.-   [3] R. R. Whitlock, Advances in X-Ray Analysis, Volume 40, 1996.

What is claimed is:
 1. A method for diagnosing a condition of an engine,the method comprising: receiving a plurality of particles fromlubricating fluid of the engine; obtaining, using data acquisitionequipment, input data associated with the plurality particles, the inputdata representative of a respective volume of and a respective chemicalcomposition category for each of the plurality of particles; using oneor more data processors: from the input data, generating datarepresentative of a mass of material of a selected one of the chemicalcomposition categories in the plurality of particles by using therespective volumes of the particles in the selected chemical compositioncategory and a density of the selected chemical composition category;comparing the mass of material of the selected chemical compositioncategory in the plurality of particles with reference data; and based onthe comparison, generating output data representative of a diagnosis ofthe condition of the engine.
 2. The method as defined in claim 1,wherein the reference data comprises historical data associated with theengine or historical data associated with one or more other engines. 3.The method as defined in claim 1, wherein the selected chemicalcomposition category is representative of an element.
 4. The method asdefined in claim 1, wherein the selected chemical composition categoryis representative of a material type.
 5. The method as defined in claim1, wherein the selected chemical composition category is representativeof an alloy.
 6. The method as defined in claim 1, comprising computingthe volume of at least one of the particles by computing a volume of anellipsoid or of rectangular prism.
 7. The method as defined in claim 1,wherein the mass of material is a cumulative mass of all the material ofthe selected chemical composition category in the plurality ofparticles.
 8. The method as defined in claim 1, wherein the referencedata comprises predefined engine diagnosis rules obtained from adatabase.
 9. The method as defined in claim 1, wherein generating datarepresentative of the mass of material includes prorating a mass of theparticles in the selected chemical composition category based on achemical composition of the particles in the selected chemicalcomposition category.
 10. A system for diagnosing a condition of anengine, the system comprising one or more data processors and one ormore memory(ies) containing machine-readable instructions for executionby the one or more data processors, the machine-readable instructionsbeing configured to cause the one or more data processors to carry outthe method performed by the one or more processors of claim
 1. 11. Anon-transitory computer-readable medium or media embodyingcomputer-executable instructions configured for causing one or more dataprocessors to carry out the method performed by the one or moreprocessors of claim
 1. 12. A method for diagnosing a condition of a gasturbine engine, the method comprising: receiving a plurality ofparticles from lubricating fluid of the gas turbine engine; using theplurality of particles, generating input data representative of arespective volume of and a respective chemical composition category foreach of the plurality of particles; using the input data and a densityof a selected one of the chemical composition categories, computing acumulative mass of material of the selected chemical compositioncategory in the plurality of particles; comparing the cumulative mass ofmaterial of the selected chemical composition category with referencedata; and based on the comparison, generating output data representativeof a diagnosis of the condition of the engine.
 13. The method as definedin claim 12, comprising using the output data to initiate a correctiveaction.
 14. The method as defined in claim 12, wherein the referencedata comprises predefined engine diagnosis rules obtained from adatabase.
 15. The method as defined in claim 12, wherein the particleshave a size between 0.5 μm and 1600 μm.